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Run LLM Locally

Llama 3.2 3B Instruct on the RX 7900 XTX

With 24 GB of VRAM, the RX 7900 XTX runs Llama 3.2 3B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 3.8 GB needed). At Q8_0 it fits entirely.

Llama 3.2 3B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 3.8 GB and fits in the 23.3 GB available, with 19.4 GB to spare.

Your hardware
RX 7900 XTX 24 GB · 64 GB RAM
Estimated memory
~3.8 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
155–259 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (960 GB/s) and model size

Memory breakdown

~3.8 GB (3.5 GB–4.2 GB) needed · estimate

Fits entirely in VRAM · 19.4 GB left

  • Model weights1.9 GB
  • KV cache0.9 GB
  • Runtime overhead0.8 GB
  • Safety margin0.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (ROCm) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RX 7900 XTX

Llama 3.2 3B Instruct on RX 7900 XTX + 64 GB RAM · 8K context
Llama 3.2 3B Instruct Q8_08K~5.2 GB · 117–185 tok/s estimateExcellent
Llama 3.2 3B Instruct Q6_K8K~4.4 GB · 136–220 tok/s estimateExcellent
Llama 3.2 3B Instruct Q5_K_M8K~4.1 GB · 145–238 tok/s estimateExcellent
Llama 3.2 3B Instruct Q4_K_M8K~3.8 GB · 155–259 tok/s estimateExcellent
Llama 3.2 3B Instruct IQ4_XS8K~3.7 GB · 162–274 tok/s estimateExcellent
Llama 3.2 3B Instruct Q3_K_M8K~3.5 GB · 167–286 tok/s estimateExcellent

Alternatives

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Radeon RX 7900 XTX

24 GB GDDR6 · 960 GB/s · 355 W

ExcellentRuns Llama 3.2 3B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RX 7900 XTX run Llama 3.2 3B Instruct?
The model needs about 3.8 GB and fits in the 23.3 GB available, with 19.4 GB to spare.
Which quantization should I use on the RX 7900 XTX?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~5.2 GB / 23.3 GB.
What is the largest context on the RX 7900 XTX?
At Q4_K_M, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Llama 3.2 3B Instruct (RX 7900 XTX)?
We estimate 155–259 tok/s (estimate, confidence low). Based on: memory bandwidth (960 GB/s) and model size.